Dominic Groß

Robust adaptive fault detection using global state information and application to mobile working machines

In this paper, an observer-based fault detection approach for a class of nonlinear systems is presented, which can be modeled by Takagi-Sugeno (TS) fuzzy models. We propose a sliding mode fuzzy observer that deals with bounded uncertainties in the plant and allows fault estimation based on an equivalent output error injection approach [3]. Furthermore an adaption scheme based on pattern recognition algorithms is presented. It allows to deal with situational uncertainties, which affect the system, by adapting the fault sensitivity. An extensive simulation of a mobile working machine is used to demonstrate the effectiveness of the proposed scheme.

Control of Uncertain Nonlinear Systems using Ellipsoidal Reachability Calculus

Abstract This paper proposes an approach to algorithmically synthesize control strategies for discrete-time nonlinear uncertain systems based on reachable set computations using the ellipsoidal calculus. For given ellipsoidal initial sets and bounded ellipsoidal disturbances, the proposed algorithm iterates over conservatively approximating and LMI-constrained optimization problems to compute stabilizing controllers. The method uses first-order Taylor approximation of the nonlinear dynamics and a conservative approximation of the Lagrange remainder. An example for illustration is included.

Robust Distributed Predictive Control of Communicating and Constrained Systems

Abstract This paper proposes a strategy for distributed model predictive control for linear time invariant systems coupled via a common cost function and constraints. Each subsystem solves a local robust model predictive control (RMPC) problem which considers uncertain predictions received from other subsystems. In the chosen communication scheme, subsystems exchange information about optimized local trajectories and time-varying disturbances sets obtained from the local RMPC. The latter optimizes over feedback policies based on a cost function motivated by min-max approaches and can be implemented as a single tractable quadratic program. Under some assumptions, the proposed scheme remains feasible even in the presence of delays and packet loss. Simulation results for a platoon of autonomous vehicles in a leader-follower scenario illustrate the approach.

Distributed and Networked Model Predictive Control

In this chapter, we consider the problem of controlling networked and distributed systems by means of model predictive control (MPC). The basic idea behind MPC is to repeatedly solve an optimal control problem based on a model of the system to be controlled. Every time a new measurement is available, the optimization problem is solved and the corresponding input sequence is applied until a new measurement arrives. As explained in the sequel, the advantages of MPC over other control strategies for networked systems are due to the fact that a model of the system is available at the controller side, which can be used to compensate for random bounded delays. At the same time, for each iteration of the optimization problem an optimal input sequence is calculated. In case of packet dropouts, one can reuse this information to maintain closed-loop stability and performance.

On the steady-state behavior of a nonlinear power system model

Abstract In this article, we consider a dynamic model of a three-phase power system including nonlinear generator dynamics, transmission line dynamics, and static nonlinear loads. We define a synchronous steady-state behavior which corresponds to the desired nominal operating point of a power system and obtain necessary and sufficient conditions on the control inputs, load model, and transmission network, under which the power system admits this steady-state behavior. We arrive at a separation between the steady-state conditions of the transmission network and generators, which allows us to recover the steady-state of the entire power system solely from a prescribed operating point of the transmission network. Moreover, we constructively obtain necessary and sufficient steady-state conditions based on network balance equations typically encountered in power flow analysis. Our analysis results in several necessary conditions that any power system control strategy needs to satisfy.

Event-Based Communication in Distributed Model Predictive Control

Abstract We investigate a scheme for event-based communication in distributed model predictive control (DMPC) of dynamically coupled linear discrete-time systems. In DMPC, a key issue is to determine how and when to communicate information between local controllers. Instead of considering a given and static communication scheme, we propose an approach to balance communication load and performance. First-order sensitivities of the local MPC solutions with respect to communicated information are used to decide whether communication between controllers may lead to improved performance. Zusammenfassung In diesem Artikel wird eine Methode zur verteilten modellprädiktiven Regelung (MPR) mit ereignisbasierter Kommunikation vorgestellt. Eine zentrale Frage bei verteilter MPR ist, wie und wann Informationen zwischen lokalen Reglern zu tauschen sind. Anstelle eines vorgegebenen statischen Kommunikationsschemas wird hier ein Ansatz betrachtet, in dem nur dann kommuniziert wird, wenn dies die Regelgüte verbessern kann. Dadurch wird ein Kompromiss zwischen Regelgüte und Auslastung des Kommunikationsnetzwerks erzielt.

Virtual Inertia Placement in Electric Power Grids

The past few years have witnessed a steady shift in the nature of power generation worldwide. While the share of renewable-based distributed generation has been on the rise, there has also been a decline in the conventional synchronous-based generation. The renewable-based power generation interfaced to the grid via power-electronic converters, however, does not provide rotational inertia, an inherent feature of synchronous machines. This absence of inertia has been highlighted as the prime source for the increasing frequency violations and severely impacting grid stability. As a countermeasure, virtual or synthetic inertia and damping emulated by advanced control techniques have been proposed. In this chapter, we study the optimal placement and tuning of these devices. We discuss two approaches based on the control notion of \(\mathcal H_2\) system gain characterizing the amplification of a disturbance and the spectral notion of pole-placement. A comprehensive analysis accompanied by iterative gradient-based algorithms is presented for both the approaches and validated on a three-area test case for comparison.

Distributed Predictive Control for a Class of Hybrid Systems with Event-Based Communication

Abstract This paper proposes a strategy for distributed model predictive control (DMPC) with event-based communication for piecewise affine (PWA) systems coupled via a common cost function and, possibly non-convex, constraints. In order to achieve cooperation between subsystems, each subsystem optimizes over its own inputs and the inputs of directly interconnected systems. In the proposed scheme, all subsystems optimize in parallel in each time step, and the subsystems only communicate information to interconnected subsystems if a triggering condition is met. Under some assumptions on a terminal control law, recursive feasibility and stability of the scheme are established. Simulation results for a platoon of autonomous vehicles are used to illustrate the approach.